Dev Prince 📊

From learning Excel… to actually using it for analysis 📊 Completed the Data Analysis in Excel course on DataCamp. Worked with: • Pivot Tables • Logical Functions • What-If Analysis • Forecasting But the real shift: Not just how tools work… But when and why to use them.

@sheCodezArt Haven't been active for some days

@_dev_prince Where you been?

I neva sleep since. Man, the dark circles around my eyes are getting bigger... wider...? Well worse dammit!

Sigh 😮‍💨 Showing up is hard

@Lordweb111 Bobby Fischer. I got a lot of questions.

If you could bring one Grandmaster back to life who would that be?

@Lordweb111 But there are so many variation in all this opening

In my opinion you should have ideas on the following opening; 1.Italian game 2.Spanish opening 3.Sicilian defense 4.Caro-kann 5.French defense 6.Queens Gambit 7. London system 8. Petrov defense 9. English opening 10. Scotch opening

Chess feels like a growing tree ♟️🌳 Every move creates more possibilities. The middle game becomes chaos. Then pieces disappear… and the tree starts shrinking. Until only one path remains: Checkmate.

Life is full of differences 📊 Different people. Different outcomes. Different groups. But statistics asks a deeper question: Are those differences truly significant… or just random chance? That’s the idea behind ANOVA and the F-test.

😌

You’ve made it through days you thought you wouldn’t. Don’t forget that when things feel heavy again. GM, especially to those who say it back👀

@_devHelen Gm Helen

@vheeorji22 Totally agree

Tiny improvements every day still count.

@vheeorji22 Hi Vivian, do you mind sharing which course you are taking?

DAY 17 OF MY JOURNEY INTO DATA ANALYTICS ( How to think Like an Analyst) I learnt : (Predictive Thinking ) 1. Predictive thinking. it is pattern recognition applied forward 2. Every prediction is built on a trend, a pattern or a relationship between two things 3. There are 3 types of predictions: direction, magnitude and conditional a. direction = which way will it go b. magnitude = by how much c. conditional = if this happens then that happens 4. I also learnt that we should always state the assumption your prediction is based on 5. Also,our past data is not just a record,it is a map of what happens next

@Lordweb111 Check out chesswithakeem on youtube

Hi Chess Players, How do you normally train or practice chess? Let's learn from you 😉

Data Analytics X has some of the most disciplined learners I’ve seen People spending hours learning SQL, fixing dashboards, cleaning datasets, and improving every single day.

@vheeorji22 While some of us struggle, you guys should share your secret. Showing up, yes, but getting to actually analyze, that's a different ball game

Why do most data people reach for Python over R? 📊 R was literally built for statistical computing. Vectorized operations come out of the box. In Python, you reach for NumPy first. And before someone says “libraries”… R has a rich ecosystem too. So what made Python win?

Data minds, I don't know who needs to hear this but your Analysis does not always have to end as a Dashboard. It could be a report Let's talk about it 🧵👇 Retweet for others

Statistician mode activated 📊

@stellaanalyzes Congratulations Stella

landed my first freelance gig through a recommendation and even got review from the team.✨ can’t disclose the contents due to NDA(Non-Disclosure Agreement)

@nikku762 @vheeorji22 Can I just learn one too and focus on thinking and problem solving? My priority are fixed on that..am I doing it wrong?

@vheeorji22 Tools help you enter the field. Thinking and problem solving help you grow in it 💯

In 2026, I’m realizing something important about Data Analytics: It’s not just about knowing tools, it’s about knowing how to think with them. Here’s what high-value Data Analysts are doing 👇: SQL → not just querying data, but pulling the exact truth from messy systems Python → not just coding, but automating boring work and speeding up insights Power BI → not just building dashboards, but turning data into clear decisions Storytelling → not just presenting charts, but making people care about the numbers And beyond tools, I’m learning that the real difference comes from: • Understanding the business problem before touching data • Cleaning and shaping data like it actually matters (because it does) • Asking better questions, not just answering the obvious ones • Communicating insights in a way that leads to action At this stage, I’m still learning but one thing is clear: tools get you started, thinking gets you hired.